weather-trends.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The weather in Zurich\n",
    "\n",
    "---\n",
    "\n",
    "[Table of contents](index.ipynb)\n",
    "\n",
    "---\n",
    "\n",
    "There are several weather stations placed around the city of Zurich. One of them is operated by the \"Wasserschutzpolizei\". It has been collecting detailed weather data for several years and is placed close to Mythenquai.\n",
    "\n",
    "We can get temperature, humidity, rain fall, solar radiation, and more for each 10minute period of the last few years. We can even get a reading of the water level of the lake!\n",
    "\n",
    "In this notebook we will look at how the temperature and humidity vary over the year. We will also count the number of rainy days in 2016.\n",
    "\n",
    "The year 2017 is not over yet, so we will use data from 2016 instead.\n",
    "\n",
    "To get started we import a few libraries that we will need later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import urllib\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately the data is not stored in a tidy CSV file. Instead we have to construct a fairly complicated URL, then \"open\" it, download the web page, and then convert it into a `pandas` data frame. This sounds more complicated then it really is.\n",
    "\n",
    "You can visit the webpage yourself: https://www.tecson-data.ch/zurich/mythenquai/. You will have to select what data (temperature, humidity, etc) you would like to see and for which days.\n",
    "\n",
    "If you dig into what your webbrowser does when you click all the buttons you will find that it constructs a URL very similar to the one shown below. Don't worry if it doesn't all make sense.\n",
    "\n",
    "For each of the measurements we want to download we list it (in German) as an additional `felder[]=` part in the URL. The last bit saves everything into a local file. This way we do not have to download it again and again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = ('messw_beg=01.01.2016&messw_end=31.12.2016&'\n",
    "        'felder[]=Temp2m&felder[]=TempWasser&felder[]=Windchill&'\n",
    "        'felder[]=LuftdruckQFE&felder[]=Regen&felder[]=Taupunkt&'\n",
    "        'felder[]=Strahlung&felder[]=Feuchte&felder[]=Pegel&'\n",
    "        'auswahl=2&combilog=mythenquai&suchen=Werte anzeigen')\n",
    "data = data.encode('ascii')\n",
    "\n",
    "req = urllib.request.Request(\n",
    "    'https://www.tecson-data.ch/zurich/mythenquai/uebersicht/messwerte.php',\n",
    "    method='POST',\n",
    "    data=data,\n",
    "    headers={\"Content-Type\": \"application/x-www-form-urlencoded\",\n",
    "             'User-Agent': 'http://github.com/wildtreetech/explore-open-data'\n",
    "            },\n",
    "    )\n",
    "\n",
    "with urllib.request.urlopen(req) as web:\n",
    "    with open('weather-2016.html', 'w') as local:\n",
    "        local.write(web.read().decode('iso-8859-1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can look at the contents of the [`weather-2016.html` file](weather-2016.html), it contains all sorts of instructions specifying the look of the webpage, and all of our data. At first sight extracting our data looks like a big task. Luckily this is such a common task that the `pandas` library has some handy functions that do most of the work for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_html('weather-2016.html',\n",
    "                  # a little hint to find the data in the file\n",
    "                  attrs={'border': '1'},\n",
    "                  # skip the first row of the data, it is junk\n",
    "                  skiprows=1,\n",
    "                  # convert all dates we find into datetime objects\n",
    "                  parse_dates=True,\n",
    "                  # and use the first column as the index, this corresponds to the date\n",
    "                  index_col=0)\n",
    "df = df[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have loaded the data, let's take a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:10:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:20:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:30:00</th>\n",
       "      <td>6.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>977.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:40:00</th>\n",
       "      <td>6.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>977.1</td>\n",
       "      <td>0.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                       1    2    3      4    5    6  7   8       9\n",
       "0                                                                 \n",
       "2016-01-01 00:00:00  6.3  7.2  6.3  977.0  0.0  6.0  0  98  405.79\n",
       "2016-01-01 00:10:00  6.3  7.2  6.3  977.0  0.0  6.0  0  98  405.79\n",
       "2016-01-01 00:20:00  6.3  7.2  6.3  977.0  0.0  6.0  0  98  405.79\n",
       "2016-01-01 00:30:00  6.4  7.2  6.4  977.1  0.0  6.1  0  98  405.78\n",
       "2016-01-01 00:40:00  6.4  7.2  6.4  977.1  0.0  6.2  0  98  405.79"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see lots of values from January, 1st 2016 ... but without names for the columns we can only guess what they really mean. Let's fix that by setting the column names explicitly. How do we know this is the correct way to label them? I compared the HTML we downloaded with the values in our table above and wrote out the names by hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "      <th>WaterTemp</th>\n",
       "      <th>Windchill</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Dewpoint</th>\n",
       "      <th>Radiation</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Waterlevel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:10:00</th>\n",
       "      <td>6.3</td>\n",
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       "      <td>977.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:20:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
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       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:30:00</th>\n",
       "      <td>6.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>977.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:40:00</th>\n",
       "      <td>6.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>977.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp  WaterTemp  Windchill  Pressure  Rain  Dewpoint  \\\n",
       "0                                                                           \n",
       "2016-01-01 00:00:00   6.3        7.2        6.3     977.0   0.0       6.0   \n",
       "2016-01-01 00:10:00   6.3        7.2        6.3     977.0   0.0       6.0   \n",
       "2016-01-01 00:20:00   6.3        7.2        6.3     977.0   0.0       6.0   \n",
       "2016-01-01 00:30:00   6.4        7.2        6.4     977.1   0.0       6.1   \n",
       "2016-01-01 00:40:00   6.4        7.2        6.4     977.1   0.0       6.2   \n",
       "\n",
       "                     Radiation  Humidity  Waterlevel  \n",
       "0                                                     \n",
       "2016-01-01 00:00:00          0        98      405.79  \n",
       "2016-01-01 00:10:00          0        98      405.79  \n",
       "2016-01-01 00:20:00          0        98      405.79  \n",
       "2016-01-01 00:30:00          0        98      405.78  \n",
       "2016-01-01 00:40:00          0        98      405.79  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['Temp', 'WaterTemp', 'Windchill', 'Pressure', 'Rain',\n",
    "              'Dewpoint', 'Radiation', 'Humidity', 'Waterlevel']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks much better. About 6degree Celcius in the middle of the night in January makes sense, as does the fact that the solar radiation is zero. It is dark after all.\n",
    "\n",
    "Let's get started with plotting the average temperature of each week:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kCjW3nJ4IYWw6lmPjUCtWtjlwOiu9U6ltiEfFf2fXwCQuXebR7BaqhWa7Gd5Q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\nAwxyBhcJmYpRBr0OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d49438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperature = df['Temp']\n",
    "temperature.resample('W').mean().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks weird. An average temperature of 15C in January is unlikely, let's zoom in on the first few days of the year. Maybe we can see what is going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HJE2ebGb+BKM30GOr6gGoKfEwobyAeROi9wwaCTUGf5BzyGqSN3SDkJbII1gU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\nlIAgCEIO8/8BaMjaj/J9Pm0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0c9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperature[:\"20160115\"].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something is not right ... the temperature changes abruptly from around 5C to over 10C. It turns out that the way the dates are formatted in our data means pandas has to guess whether the date is in European (day first) or American (month first) formatting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-01-08 00:00:00')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('01.08.2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-08-01 00:00:00')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('01.08.2016', dayfirst=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, instead of letting pandas guess the format we need to specify it explicitly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attempt two\n",
    "\n",
    "next we load the data without trying to parse the dates automatically. Instead we specify explicitly the format (day first) when we convert the first column to a datetime. The first bit of code is as above, but we remove our request to parse the dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_html('weather-2016.html',\n",
    "                  attrs={'border': '1'},\n",
    "                  skiprows=1)\n",
    "df = df[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we explicitly convert the first column of our dataset into a date, specifying that the day comes first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[0] = pd.to_datetime(df[0], dayfirst=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And as before we set the names of the columns and then specify that we want to use the date column as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = ['Date', 'Temp', 'WaterTemp', 'Windchill', 'Pressure', 'Rain',\n",
    "              'Dewpoint', 'Radiation', 'Humidity', 'Waterlevel']\n",
    "df = df.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "      <th>WaterTemp</th>\n",
       "      <th>Windchill</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Dewpoint</th>\n",
       "      <th>Radiation</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Waterlevel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:10:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:20:00</th>\n",
       "      <td>6.3</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>977.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:30:00</th>\n",
       "      <td>6.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>977.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:40:00</th>\n",
       "      <td>6.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>977.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>405.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp  WaterTemp  Windchill  Pressure  Rain  Dewpoint  \\\n",
       "Date                                                                        \n",
       "2016-01-01 00:00:00   6.3        7.2        6.3     977.0   0.0       6.0   \n",
       "2016-01-01 00:10:00   6.3        7.2        6.3     977.0   0.0       6.0   \n",
       "2016-01-01 00:20:00   6.3        7.2        6.3     977.0   0.0       6.0   \n",
       "2016-01-01 00:30:00   6.4        7.2        6.4     977.1   0.0       6.1   \n",
       "2016-01-01 00:40:00   6.4        7.2        6.4     977.1   0.0       6.2   \n",
       "\n",
       "                     Radiation  Humidity  Waterlevel  \n",
       "Date                                                  \n",
       "2016-01-01 00:00:00          0        98      405.79  \n",
       "2016-01-01 00:10:00          0        98      405.79  \n",
       "2016-01-01 00:20:00          0        98      405.79  \n",
       "2016-01-01 00:30:00          0        98      405.78  \n",
       "2016-01-01 00:40:00          0        98      405.79  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks good, how about the temperature plot of the first two weeks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d25b208>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K05KSSyZeu/UsvPWt92V8vrU+mJLB5SQoS5g8LHteg/g9trdFUjpTRoJGmq6I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\nJfx/CQq0wUYssO8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e026d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperature = df['Temp']\n",
    "temperature[:\"20160115\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much more reasonable. To check you can compare it to the values in the HTML table by hand. Now back to showing the average temperature of each week:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Revoq8CFYttEh9Pw5b++z9Hmbj539kwaO3vZWHFyqrLybtygb4zZADSPOHwN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\n3HLWTH77qdOobOziml+9yc6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A5caYFYA/G1G8DJSIyH4ReUBEznMHtl/ieu+tAH7H8BF5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e082b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperature.resample('W').mean().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks much more like what we would expect. Cold in winter and increasingly warm towards summer. After which it cools off again.\n",
    "\n",
    "So, after this detour to the world of dealing with loading data and parsing dates we did not yet look at the humidity and rainy days. Because there is quite a bit of code involved in fetching and loading the data there is a helper function in [`utils.py`](/edit/utils.py) called `get_weather_data()` that takes care of everything for us.\n",
    "\n",
    "To find out about rainy days in Zurich, go to the next notebook: [Zurich weather exploration](zurich-weather-exploration.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}